Comprehensive Study Toward Energy Opportunity for Buildings Considering Potentials for Using Geothermal and Predicting Chiller Demand

Date of Award

2020

Degree Name

Ph.D. in Mechanical Engineering

Department

Department of Mechanical and Aerospace Engineering and Renewable and Clean Energy

Advisor/Chair

Advisor: Kevin P Hallinan

Abstract

This dissertation focusses mainly on loads determination, building informatics, and geothermal energy systems. The first chapter is Low-Energy Opportunity for Multi-Family Residences: A Simulation-Based Study of a Borehole Thermal Energy Storage System. In this chapter, we propose a district borehole thermal solar energy storage (BTES) system for both retrofit and new construction for a multi-family residence in the Midwestern United States, where the climate is moderately cold with very warm summers. Actual apartment interval power and water demand data was mined and used to estimate unit level hourly space and water heating demands, which was subsequently used to design a cost-optimal BTES system. Using a dynamic simulation model to predict the system performance over a 25-year period, a parametric study was conducted that varied the sizes of the BTES system and the solar collector array. A life-cycle cost analysis concluded that is it possible for an optimally-sized system to achieve an internal rate of return (IRR) of 11%, while reducing apartment-wide energy and carbon consumption by 46% The promise for district-scale adoption of BTES in multi-family residences is established, particularly for new buildings. In the second chapter (Alternate Approach to the Calculation of Thermal Response Factors for Vertical Borehole Ground Heat Exchanger Arrays Using an Incomplete Bessel Function), we presents another methodology for the calculation of dimensionless thermal response factors for vertical borehole ground heat exchanger (GHX) arrays, which is a concept introduced by Eskilson (1987). The presented method is based on a well-known solution to an analogous problem in the field of well hydraulics. This solution method, known mathematically as an incomplete Bessel function, and known in the field of well hydraulics as the `leaky aquifer function', describes the hydraulic head distribution in an aquifer with predominantly radial flow to a well combined with vertical `leakage' from geologic layers above and below the pumped aquifer. The solution is adapted to model heat transfer from an array of arbitrarily-placed vertical boreholes of finite depth. With proper expression of parameters in the incomplete Bessel function, we show that g-functions of previous researchers can be approximated. The proposed method has been implemented into Matlab and Excel/VBA for g-function generation and monthly GHX simulation.Chapter three (Energy Data Mining to Predict Chiller Demand) discusses the existing methods for predicting the cooling load (physical based models, data based models, and hybrid models). It also, consider the concerns raises about how researchers have defined the cooling load when utilizing data-based models to predict cooling demand. In this context, the goal of chapter three is to demonstrate the value of data-based modeling to estimating savings from improved HVAC systems and controls, especially focusing on cooling load prediction. Here, it is assumed that interval building demand data is available. As well, short-term interval data is available for the chiller. Such data could be collected from a short-term data-logging effort by an energy service company. In the end, the goal is to utilize data-based modeling to relate chiller demand to whole building demand. If a model could be developed to accurately predict chiller power from whole building demand, then chiller health could be assessed in the future simply from whole building demand. Finally, in the last chapter (Predicting Chiller Running Capacity for School Buildings Using Stacking Learning) we tried to cover in depth most of the issues that researchers face when dealing with real-world data and to provide a clear road map for utilizing machine learning in energy engineering applications with that help reduce prediction error and reduce over-fitting from data, algorithm, and process considerations. This include data pre-processing, data balance, data subset, algorithmic considerations, ensemble learning, and evaluating model performance for both classification and regression problems.

Keywords

Mechanical Engineering, Energy, Geothermal Engineering, Borehole Thermal Energy Storage, Machine Learning, Data Mining, Chiller Demand

Rights Statement

Copyright © 2020, author

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